Alleviating the New User Problem in Collaborative Filtering by Exploiting Personality Information (original) (raw)

Personality-Based Active Learning for Collaborative Filtering Recommender Systems

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user's personality -using the Five Factor Model (FFM) -in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

Towards Personality-Aware Recommendation

ArXiv, 2016

In the last decade new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. The two main reasons are: firstly, a person's buying choices are influenced by psychological factors like impulsiveness, and secondly, some consumers may be more susceptible to making impulse purchases than others. To the best of our knowledge, the impact of personality factors on advertisements has been largely neglected at the level of recommender systems. This work proposes a highly innovative research which uses a personality perspective to determine the unique associations among the consumer's buying tendency and advert recommendations. As a matter of fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of s...

Improving Recommendation Systems with User Personality Inferred from Product Reviews

arXiv (Cornell University), 2023

Personality is a psychological factor that reflects people's preferences, which in turn influences their decision-making. We hypothesize that accurate modeling of users' personalities improves recommendation systems' performance. However, acquiring such personality profiles is both sensitive and expensive. We address this problem by introducing a novel method to automatically extract personality profiles from public product review text. We then design and assess three context-aware recommendation architectures that leverage the profiles to test our hypothesis. Experiments on our two newly contributed personality datasets-Amazon-beauty and Amazon-music-validate our hypothesis, showing performance boosts of 3-28%. Our analysis uncovers that varying personality types contribute differently to recommendation performance: open and extroverted personalities are most helpful in music recommendation, while a conscientious personality is most helpful in beauty product recommendation. CCS CONCEPTS • Information systems → Recommender systems; • Applied computing → Psychology.

Improving recommendations by using personality traits in user profiles

2008

Abstract: By storing Personality Traits in User Profiles we enable Recommender Systems to deduce more interesting recommendations for users acting pro-actively in order to offer them products/services as a consequence of a prediction of their future needs and behavior. This paper is proposed to improve the robustness of recommendations by using psychological aspects such as Personality Traits. This paper is a part of a PhD ongoing work. Key Words: User Psychological Profile, Identity, Reputation, Personality Traits, Recommendation

The 50/50 recommender: personality in movie recommender systems

2017

This dissertation was written as a part of the MSc in ICT Systems at the International Hellenic University. Its main goal is the examination of the role of human personality in Movie Recommender systems. We introduce the concept of combining collaborative techniques with a personality test so to provide more personalized movie recommendations. Previous research has shown some efforts to incorporate personality in Recommender systems, but no actual implementation has been attempted on a software level. Using a renowned movie dataset and the Big Five Personality test, we developed a system with Python that managed to improve the normal Movie Recommendation experience by 3.62%. The findings show that Personalization improves the user's experience even though extra effort might be demanded. With further modifications and testing, we can come to the new age of recommender systems, where personality of the user is as important as it is in real life. I would like to thank my supervisor Dr. Christos Tjortjis for his guidance and his excellent ideas and additions to this Dissertation.

The 50/50 Recommender: A Method Incorporating Personality into Movie Recommender Systems

Engineering Applications of Neural Networks, 2017

Recommendation systems offer valuable assistance with selecting products and services. This work checks the hypothesis that taking personality into account can improve recommendation quality. Our main goal is to examine the role of personality in Movie Recommender systems. We introduce the concept of combining collaborative techniques with a personality test to provide more personalized movie recommendations. Previous research attempted to incorporate personality in Recommender systems, but no actual implementation appears to have been achieved. We propose a method and developed the 50/50 recommender system, which combines the Big Five personality test with an existing movie recommender, and used it on a renowned movie dataset. Evaluation results showed that users preferred the 50/50 system 3.6% more than the state of the art method. Our findings show that personalization provides better recommendations, even though some extra user input is required upfront.

Personality-Driven Social Multimedia Content Recommendation

Proceedings of the 30th ACM International Conference on Multimedia

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. Still, McKinsey-style manual categorization is a very labour-intensive task that is probably impractical in a real-world scenario, so automated incorporation of audience behaviour and personality mining into industrial applications is necessary. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Even worse, there is no dataset available for the research community to serve as a benchmark and drive further research in this direction. The present study is one of the first attempts to bridge this important industrial gap, contributing not just a novel personality-driven content recommendation approach and dataset, but also facilitating a real-world ready solution which is scalable and sufficiently accurate to be applied in real-world settings. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable

Personality-Based Matrix Factorization for Personalization in Recommender Systems

International Journal of Information and Communication Technology Research, 2022

Recommender systems are one of the most used tools for knowledge discovery in databases, and they have become extremely popular in recent years. These systems have been applied in many internet-based communities and businesses to make personalized recommendations and acquire higher profits. Core entities in recommender systems are ratings given by users to items. However, there is much additional information which using it can result in better performance. The personality of each user is one of the most useful data that can help the system produce more accurate and suitable recommendations for active users. It is noteworthy that the characteristics of a person can directly affect his/her behavior. Therefore, in this paper, the personality of users is identified, and a novel mathematical and algorithmic approach is proposed in order to utilize this information for making suitable recommendations. The base model in our proposed approach is matrix factorization, which is one of the most powerful methods in model-based recommender systems. Experimental results on MovieLens dataset demonstrate the positive impact of using personality information in the matrix factorization technique, and also reveal better performance by comparing them with the state-of-the-art algorithms.

Personality-Based Recommendation in E-Commerce

2013

In recent years there has been an exponential increase in the number of users each day adopting e-commerce as a purchasing vehicle of products and services. This has led to a growing interest from the scientific community in approaches and models that would improve the customer experience. Specifically, it has been repeatedly pointed out that the definition of a customer experience tailored to the user personality traits would likely increase the probability of purchase. In this article we illustrate a recommender system for e-commerce capable of adapting the product and service offer according to not only the user interests and preferences, and his context of use, but also his personality profile derived from information relating to his professional activities.